1. Field of the Invention
This invention relates to a method for identifying varieties of wheat or other grains by visual imaging and comparison of the characteristics of a sample thereof to a known standard. The method involves the use of a series of steps advantageously employing a computer to conduct digital image analysis of the kernel and germ in order to correctly identify the cultivar of the particular sample.
2. Description of the Prior Art
Even those largely unfamiliar with the agriculture industry recognize that wheat and other grains are further differentiated by classes and cultivars. Classes of wheat such as Hard Red Spring Wheat, Hard red Winter Wheat, Soft Red Winter Wheat and White are planted in different regions at different times, used for different purposes and command different prices at the elevator and at commodities exchanges. Within each class, a variety of different cultivars have been developed, each with its own characteristics and properties. For example, Bounty, Centura, Centurk, Mustang, Newton, Tam 105, Pike and Rough Rider are Hard Red Winter (HRW) wheat cultivars and Katepaw, Stoa, and Len are Hard Red Pring (HRS) wheat cultivars while Hill, Stephans and Lewjain are examples of White wheat cultivars. There are presently over 600 cultivars of wheat grown in the United States.
It is obviously necessary to identify the different cultivars in order to properly classify the wheat for purposes such as sale to a miller or use as seed. The responsibility for wheat classification traditionally falls to Federal Grain Inspection Service (FGIS) inspectors which are to check a representative sample of the wheat to determine the cultivar by such characteristics as brush size, germ angle, cheek angle, kernel shape, and seed coat texture. The FGIS personnel are charged with knowing the characteristics of all varieties handled within their particular market. However, the proliferation of new cultivars and the practice of crossbreeding varieties has nearly overwhelmed the capabilities of the FGIS. The current method of wheat classification, developed in 1917, is somewhat archaic and obsolete in view of the number of new variety releases each year. Thus, there is a genuine need for a rapid, accurate, more efficient and precise procedure of kernel classification and identification.
Previous studies have attempted to use kernel color as a separation method by such techniques as scanning spectrophotometry. Other efforts have employed kernel hardness, but the results are difficult to reproduce. Still other proposed methods have used electrophoresis or chromatography to isolate and characterize cereal proteins constituencies.
Attempts to identify food products by seed shape and size parameters have also been employed. These have encountered certain methodological limitations, while further studies have employed digital image analysis to study the physical dimensions of cereal crops such as rice and wheat. However, the results of these image analyses indicated that discrimination between class and cultivar were limited in terms of number of kernel characteristics used to discriminate between the classes. It thus appears that new discriminators must be employed to handle identification and classification in an efficient manner.